arXiv:2606.18543v1 Announce Type: new Abstract: Language model agents are becoming proficient executors at isolated, short-horizon tasks such as software engineering and customer service. Yet real-world challenges require a combination of sophisticated skills that remain largely untested in agents: (1) navigating long horizons amid uncertainty; (2) acquiring information in noisy environments; (3) adapting to a changing world; (4) orchestrating multiple moving parts toward a coherent goal. We introduce CEO-Bench, which evaluates these capabilities together by simulating a representative real-wo
The proliferation of language models has enabled researchers to push the boundaries of agentic capabilities, prompting a need for more sophisticated evaluation benchmarks that reflect real-world problem complexity.
Measuring the long-term, adaptive, and orchestration capabilities of AI agents is crucial for understanding their true potential and limitations, especially for complex enterprise and societal applications.
The introduction of CEO-Bench provides a new, more rigorous standard for evaluating AI agent performance beyond isolated short-horizon tasks, thereby accelerating research towards more capable and robust autonomous systems.
- · AI Agent developers
- · Enterprises adopting AI for complex workflows
- · Open-source AI research
- · AI models only optimized for short-term tasks
- · Companies with simplistic approaches to AI agent integration
CEO-Bench will drive significant advancements in AI agent architecture and training methodologies.
More capable AI agents will begin automating multi-stage, complex white-collar tasks, impacting professional service sectors.
The demonstrated ability of agents to navigate long horizons and adapt to changing conditions could lead to entirely new organizational structures and business models.
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Read at arXiv cs.AI